Reconstruction of Optical Fiber Bragg Grating Sensor Strain Distributions Using a Genetic Algorithm

T-matrix method;sensor placement;strain sensors;fiber Bragg gratings;genetic algorithms

Gill, Apninder Singh ; Dr. Kara Peters, Committee Chair,Gill, Apninder Singh ; Dr. Kara Peters ; Committee Chair

University:North Carolina State University

关键词: T-matrix method;    sensor placement;    strain sensors;    fiber Bragg gratings;    genetic algorithms;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/1765/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Optical fiber Bragg gratings are unique among embedded strain sensors due to their potential to measure strain distributions with a spatial resolution of a few nanometers over gage lengths of a few centimeters. This thesis presents a genetic algorithm for the interrogation of optical fiber Bragg grating strain sensors. The method calculates the period distribution along the Bragg grating which can then be directly related to the axial strain distribution. The period distribution is determined from the output intensity spectrum of the grating via a T-matrix approach. The genetic algorithm inversion method presented requires only intensity information and reconstructs non-linear and discontinuous distributions well, including regions with significant gradients. The method is demonstrated through example reconstructions of Bragg grating sensor simulated data. The development of this algorithm will permit the use of Bragg grating sensors for damage identification in regions close to localized damages where strong strain non-linearities occur. A second application of the genetic algorithm search procedure is also discussed, the optimization of sensor locations for a particular structural application. An implementation of the genetic algorithm to a discrete sensor location problem is presented and its performance evaluated. A combination of the developed GA with available damage identification systems can be effectively used to find the optimal sensor distribution for damage detection.